During the Industrial Age, the preferred models of human organization were vertically structured, hierarchical, and monolithic. Hierarchical organizational structures promoted efficient economies of scale and the effective command and control of participants within organizations. However, these organizational models have changed in the so-called Information Age, in which worldwide information networks have made, and are increasingly making, vast amounts of information available to all participants in real time, enabling them to make decisions and take actions far more quickly and adaptively than decision-making by top-down management in hierarchical organizations. The preferred models of organization in the information economy are horizontal, networked, and widely distributed. They are requiring a fundamental re-thinking and re-structuring of business, political, and societal organizations in the post-industrial era.
Networked organizations can be viewed as complex, non-linear, adaptive systems that operate by totally different assumptions and rules than hierarchical organizations. With relevant information being available simultaneously to all participants through all-pervasive channels (e.g., broadcast or publication media channels), decision making and taking action can occur locally at any point (node) of a network without having to traverse through the established levels or protocols of a hierarchical organization. A network can accommodate rapid growth in the addition of new participants simply by association with new nodes or to the nearest available contacts (connectors, pathways, qualitative state or hubs) in common or by exclusion, compare, or qualitative descriptor with other nodes. Participants who share common values, beliefs, or goals will prefer to aggregate in communities (clusters) and can link to the nodes of other communities having shared interests or desired or appropriate qualitative state. The preferential attachments of participants to common nodes, in clusters, and by links to other clusters allow information flow and decision making to traverse the network quickly by a relatively few number of links. If some clusters cease activity or are rendered inoperative, the pervasiveness of networked linkages ensures that other pathways for information and decision making through the network can be found. This provides the robustness of a network to adapt to changing conditions and circumstances.
However, obtaining relevant information about a network or other complex adaptive system has correspondingly become more difficult. Information is widely dispersed among a large number of nodes and clusters, and information from one node or cluster does not necessarily apply to or represent other nodes and clusters. In contrast to a top-down organization, it is also difficult to predict how the nodes and clusters of a network will react to new information or to changing conditions and circumstances. Informants linked to specific communities can better characterize the features of the linked participants of a community than external attempts to characterize the network in aggregate. Without being able to extract relevant information, the status, intentions, and future actions of a network or other complex adaptive system can be difficult to assess.
As an example, the information economy is having a fundamental impact worldwide on the ways that businesses market, distribute and sell products to consumers. Whereas businesses in the industrial era sought to dominate markets by amassing capital, resources, and distribution channels to sell standardized products to consumers, businesses today face a much more competitive environment in which real time information enables many new businesses to continually enter with new or customized products, and consumers to demand with ever increasing specificity the product features or services they want to purchase. This is fueled by vast amounts of financial, marketing, news, and other information made available simultaneously to businesses and consumers alike. Businesses must make quick decisions when to launch new products or customize existing products to changing consumer tastes, and consumers are become ever more informed about product features they want and comparison pricing for product offerings.
In a competitive market environment, the marketing director in each of the many competitor companies must decide whether, when, and what kind of product to bring to market based upon trying to assess what will succeed with consumers in relation to what products other companies will offer. His/her company's ultimate goal is to succeed by increasing sales for its products, yet must be careful not to launch the wrong product and lose consumer confidence, or to take on the market leader or other competitors too directly or too soon and risk being squashed. Their assessments must be made against a vast background of real-world data reflecting historical economic trends, product development timelines, consumers' changing attitudes, and ever shifting tactics and product releases by competitors. As a result, powerful computerized analysis tools have been very much in demand to assist and inform marketing decision making.
Prior attempts to use computer simulation to model complex systems like competitive markets have relied on modeling aggregate parameters of the modeled environment, such as cost of capital, interest rates, consumer spending, sales histories, seasonal adjustments, etc., then the probable responses of other companies, distributor intermediaries, and end users (purchasers) are deductively predicted using market rules culled from market informants. However, the use of deductive methods to forecast the actions of participants from aggregate parameters is, at best, an academic fiction because the participants do not all behave the same way over any extended length of time and with a plethora of diverse sources of market and product information.
Under real conditions, participants in a given environment can be expected to interpret real-world information available to them in different ways, and they will continually adapt and evolve new rules of decision making, showing no evidence of settling down to a single set of commonly observed rules for all participants in that environment. Moreover, complex systems have shown a tendency to coalesce toward unexpected “emergent” behavior that arises out of the chaotic ecology of the multitude of individual decisions and interactions, rather than a predictable behavior that can be deduced from the sum of their individual parts. New methods of modeling have had to be developed to take into account the individual behaviors and interactions of participants in order to derive inductively relevant information about the environment being modeled.
Recent attempts have been made to model complex systems by modeling the expected behavior of the individual participants (agents) in the environment, and running simulations based upon the chaotic ecology of their interactions. For example, the SWARM simulation system developed at the Santa Fe Institute provides general purpose tools for modeling multiple agents and running simulations of anything from artificial life to traffic patterns. See, “The SWARM Simulation System: A Tool for Studying Complex Systems”, by C. Langton, N. Minar, and R. Burkhart, Santa Fe Institute, March 1995. While such multiagent modeling systems are better at approximating the behavior of complex systems, they have still relied on modeling individual agents according to an assumed set of behaviors or rules of interaction in response to predefined initial conditions. These existing multi-agent simulation models thus do not take into account the diverse cultural interactions, values and belief bases of perception, and the unexpected responses of participants to diverse sources of real-world information under changing conditions.